class_docstr: Creates a random ill-conditioned Gaussian.
  
  The covariance matrix has eigenvalues sampled from the inverse Gamma
  distribution with the specified shape, and then rotated by a random orthogonal
  matrix.
  
  Note that this function produces reproducible targets, i.e. the constructor
  `seed` argument always needs to be non-`None`.
docstr: Generate samples of the specified shape from the target distribution.
  
  The returned samples are exact (and independent) samples from the target
  distribution of this model.
  
  Note that a call to `sample()` without arguments will generate a single
  sample.
  
  Args:
    sample_shape: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
    seed: Python integer or `tfp.util.SeedStream` instance, for seeding PRNG.
    name: Name to give to the prefix the generated ops.
  
  Returns:
    samples: a `Tensor` with prepended dimensions `sample_shape`.
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code: def sample(self, sample_shape=(), seed=None, name="sample"):
      """Generate samples of the specified shape from the target distribution.
  
      The returned samples are exact (and independent) samples from the target
      distribution of this model.
  
      Note that a call to `sample()` without arguments will generate a single
      sample.
  
      Args:
        sample_shape: 0D or 1D `int32` `Tensor`. Shape of the generated samples.
        seed: Python integer or `tfp.util.SeedStream` instance, for seeding PRNG.
        name: Name to give to the prefix the generated ops.
  
      Returns:
        samples: a `Tensor` with prepended dimensions `sample_shape`.
      """
      return self._gaussian.sample(sample_shape, seed=seed, name=name)
  
file_path: probability/spinoffs/inference_gym/inference_gym/targets/ill_conditioned_gaussian.py
imports: import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from inference_gym.targets import model
label_desc: Use image node_id 4 for calling the IllConditionedGaussian obj's underlying member method code with example usage: obj.sample(sample_shape, seed, name) and returns: self